Overview

Dataset statistics

Number of variables9
Number of observations496
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.0 KiB
Average record size in memory72.3 B

Variable types

Categorical1
Numeric8

Warnings

date has a high cardinality: 496 distinct values High cardinality
Coquimbo is highly correlated with Valparaiso and 4 other fieldsHigh correlation
Valparaiso is highly correlated with Coquimbo and 6 other fieldsHigh correlation
Metropolitana_de_Santiago is highly correlated with Coquimbo and 6 other fieldsHigh correlation
Libertador_Gral__Bernardo_O_Higgins is highly correlated with Coquimbo and 6 other fieldsHigh correlation
Maule is highly correlated with Coquimbo and 6 other fieldsHigh correlation
Biobio is highly correlated with Coquimbo and 6 other fieldsHigh correlation
La_Araucania is highly correlated with Valparaiso and 5 other fieldsHigh correlation
Los_Rios is highly correlated with Valparaiso and 5 other fieldsHigh correlation
Coquimbo is highly correlated with Valparaiso and 5 other fieldsHigh correlation
Valparaiso is highly correlated with Coquimbo and 6 other fieldsHigh correlation
Metropolitana_de_Santiago is highly correlated with Coquimbo and 6 other fieldsHigh correlation
Libertador_Gral__Bernardo_O_Higgins is highly correlated with Coquimbo and 6 other fieldsHigh correlation
Maule is highly correlated with Coquimbo and 6 other fieldsHigh correlation
Biobio is highly correlated with Coquimbo and 6 other fieldsHigh correlation
La_Araucania is highly correlated with Coquimbo and 6 other fieldsHigh correlation
Los_Rios is highly correlated with Valparaiso and 5 other fieldsHigh correlation
Coquimbo is highly correlated with Valparaiso and 2 other fieldsHigh correlation
Valparaiso is highly correlated with Coquimbo and 5 other fieldsHigh correlation
Metropolitana_de_Santiago is highly correlated with Coquimbo and 6 other fieldsHigh correlation
Libertador_Gral__Bernardo_O_Higgins is highly correlated with Coquimbo and 6 other fieldsHigh correlation
Maule is highly correlated with Valparaiso and 5 other fieldsHigh correlation
Biobio is highly correlated with Valparaiso and 5 other fieldsHigh correlation
La_Araucania is highly correlated with Valparaiso and 5 other fieldsHigh correlation
Los_Rios is highly correlated with Metropolitana_de_Santiago and 4 other fieldsHigh correlation
Libertador_Gral__Bernardo_O_Higgins is highly correlated with La_Araucania and 6 other fieldsHigh correlation
La_Araucania is highly correlated with Libertador_Gral__Bernardo_O_Higgins and 5 other fieldsHigh correlation
Valparaiso is highly correlated with Libertador_Gral__Bernardo_O_Higgins and 6 other fieldsHigh correlation
Metropolitana_de_Santiago is highly correlated with Libertador_Gral__Bernardo_O_Higgins and 6 other fieldsHigh correlation
Maule is highly correlated with Libertador_Gral__Bernardo_O_Higgins and 6 other fieldsHigh correlation
Los_Rios is highly correlated with Libertador_Gral__Bernardo_O_Higgins and 5 other fieldsHigh correlation
Biobio is highly correlated with Libertador_Gral__Bernardo_O_Higgins and 6 other fieldsHigh correlation
Coquimbo is highly correlated with Libertador_Gral__Bernardo_O_Higgins and 4 other fieldsHigh correlation
date is uniformly distributed Uniform
date has unique values Unique
Maule has unique values Unique
Biobio has unique values Unique
La_Araucania has unique values Unique
Los_Rios has unique values Unique
Coquimbo has 5 (1.0%) zeros Zeros
Valparaiso has 7 (1.4%) zeros Zeros
Libertador_Gral__Bernardo_O_Higgins has 8 (1.6%) zeros Zeros

Reproduction

Analysis started2021-08-02 02:35:08.101387
Analysis finished2021-08-02 02:35:14.040956
Duration5.94 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

date
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct496
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1989-02-01
 
1
2002-07-01
 
1
1988-09-01
 
1
2012-06-01
 
1
2006-06-01
 
1
Other values (491)
491 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters4960
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique496 ?
Unique (%)100.0%

Sample

1st row1989-02-01
2nd row1998-10-01
3rd row2001-02-01
4th row2008-11-01
5th row2009-04-01

Common Values

ValueCountFrequency (%)
1989-02-011
 
0.2%
2002-07-011
 
0.2%
1988-09-011
 
0.2%
2012-06-011
 
0.2%
2006-06-011
 
0.2%
1989-06-011
 
0.2%
2007-05-011
 
0.2%
2011-04-011
 
0.2%
1993-02-011
 
0.2%
1987-08-011
 
0.2%
Other values (486)486
98.0%

Length

2021-08-01T22:35:14.213564image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1989-02-011
 
0.2%
2002-07-011
 
0.2%
1988-09-011
 
0.2%
2012-06-011
 
0.2%
2006-06-011
 
0.2%
1989-06-011
 
0.2%
2007-05-011
 
0.2%
2011-04-011
 
0.2%
1993-02-011
 
0.2%
1987-08-011
 
0.2%
Other values (486)486
98.0%

Most occurring characters

ValueCountFrequency (%)
01326
26.7%
11122
22.6%
-992
20.0%
9473
 
9.5%
2379
 
7.6%
8209
 
4.2%
7101
 
2.0%
490
 
1.8%
390
 
1.8%
589
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3968
80.0%
Dash Punctuation992
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01326
33.4%
11122
28.3%
9473
 
11.9%
2379
 
9.6%
8209
 
5.3%
7101
 
2.5%
490
 
2.3%
390
 
2.3%
589
 
2.2%
689
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
-992
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4960
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01326
26.7%
11122
22.6%
-992
20.0%
9473
 
9.5%
2379
 
7.6%
8209
 
4.2%
7101
 
2.0%
490
 
1.8%
390
 
1.8%
589
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII4960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01326
26.7%
11122
22.6%
-992
20.0%
9473
 
9.5%
2379
 
7.6%
8209
 
4.2%
7101
 
2.0%
490
 
1.8%
390
 
1.8%
589
 
1.8%

Coquimbo
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct492
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.15469103
Minimum0
Maximum347.1338889
Zeros5
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-08-01T22:35:14.302312image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.06191503269
Q10.8215212434
median4.071035946
Q316.13759968
95-th percentile68.23522061
Maximum347.1338889
Range347.1338889
Interquartile range (IQR)15.31607844

Descriptive statistics

Standard deviation32.62928614
Coefficient of variation (CV)2.01980255
Kurtosis35.85786972
Mean16.15469103
Median Absolute Deviation (MAD)3.818003267
Skewness4.969772006
Sum8012.726752
Variance1064.670314
MonotonicityNot monotonic
2021-08-01T22:35:14.408341image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05
 
1.0%
17.263150331
 
0.2%
5.0711111131
 
0.2%
11.719176471
 
0.2%
0.7810718951
 
0.2%
7.3607189551
 
0.2%
112.17401321
 
0.2%
0.26033986981
 
0.2%
4.6290653571
 
0.2%
7.0160326881
 
0.2%
Other values (482)482
97.2%
ValueCountFrequency (%)
05
1.0%
0.00018954247821
 
0.2%
0.00032026144411
 
0.2%
0.00071895426391
 
0.2%
0.0034509803921
 
0.2%
0.0044444444131
 
0.2%
0.0080653594991
 
0.2%
0.0091372549621
 
0.2%
0.01051633971
 
0.2%
0.011209150471
 
0.2%
ValueCountFrequency (%)
347.13388891
0.2%
270.43728731
0.2%
260.33306551
0.2%
147.16290851
0.2%
137.9755491
0.2%
136.21232691
0.2%
135.65498041
0.2%
120.45303921
0.2%
113.78549681
0.2%
112.17401321
0.2%

Valparaiso
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct490
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.45098021
Minimum0
Maximum441.0486039
Zeros7
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-08-01T22:35:14.520214image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03691558428
Q11.53816153
median9.037824679
Q338.4092126
95-th percentile127.953535
Maximum441.0486039
Range441.0486039
Interquartile range (IQR)36.87105107

Descriptive statistics

Standard deviation55.43433032
Coefficient of variation (CV)1.708248255
Kurtosis16.15727121
Mean32.45098021
Median Absolute Deviation (MAD)8.807775978
Skewness3.418567302
Sum16095.68618
Variance3072.964978
MonotonicityNot monotonic
2021-08-01T22:35:14.619003image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07
 
1.4%
0.53413960841
 
0.2%
0.66266233771
 
0.2%
6.440909091
 
0.2%
21.938133121
 
0.2%
95.179951261
 
0.2%
78.170584391
 
0.2%
25.314237011
 
0.2%
14.217418861
 
0.2%
7.0890909161
 
0.2%
Other values (480)480
96.8%
ValueCountFrequency (%)
07
1.4%
4.870129761 × 10-51
 
0.2%
0.00024350650321
 
0.2%
0.00040584415581
 
0.2%
0.00066558441561
 
0.2%
0.0022240259761
 
0.2%
0.0033603895021
 
0.2%
0.0037012987011
 
0.2%
0.017987013031
 
0.2%
0.019610389451
 
0.2%
ValueCountFrequency (%)
441.04860391
0.2%
404.70165591
0.2%
369.65163971
0.2%
353.76402571
0.2%
307.36631461
0.2%
245.58319791
0.2%
243.13420451
0.2%
223.68287331
0.2%
201.38704561
0.2%
197.5542531
0.2%

Metropolitana_de_Santiago
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct494
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.66668249
Minimum0
Maximum524.5926671
Zeros3
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-08-01T22:35:14.721566image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2132802003
Q15.297546145
median19.08759229
Q363.40836403
95-th percentile186.6450672
Maximum524.5926671
Range524.5926671
Interquartile range (IQR)58.11081789

Descriptive statistics

Standard deviation74.36109868
Coefficient of variation (CV)1.497202852
Kurtosis10.79959059
Mean49.66668249
Median Absolute Deviation (MAD)17.29051175
Skewness2.861950599
Sum24634.67451
Variance5529.572996
MonotonicityNot monotonic
2021-08-01T22:35:14.832282image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03
 
0.6%
0.028271812071
 
0.2%
192.09484911
 
0.2%
19.893103981
 
0.2%
25.707416091
 
0.2%
162.31657731
 
0.2%
126.27241641
 
0.2%
51.469865831
 
0.2%
25.662231591
 
0.2%
27.66781881
 
0.2%
Other values (484)484
97.6%
ValueCountFrequency (%)
03
0.6%
0.0026342282011
 
0.2%
0.0062080536461
 
0.2%
0.0093959732441
 
0.2%
0.010184563871
 
0.2%
0.011493288231
 
0.2%
0.011862416081
 
0.2%
0.01587248341
 
0.2%
0.021140939861
 
0.2%
0.028271812071
 
0.2%
ValueCountFrequency (%)
524.59266711
0.2%
481.12971511
0.2%
475.46333931
0.2%
437.63340611
0.2%
417.06937931
0.2%
344.10654351
0.2%
329.3928351
0.2%
306.33661081
0.2%
296.62609121
0.2%
288.91771791
0.2%

Libertador_Gral__Bernardo_O_Higgins
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct489
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.24980496
Minimum0
Maximum645.5157516
Zeros8
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-08-01T22:35:14.938998image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.07898837202
Q15.343992242
median25.99596898
Q3101.8750929
95-th percentile275.7165937
Maximum645.5157516
Range645.5157516
Interquartile range (IQR)96.53110066

Descriptive statistics

Standard deviation101.4261116
Coefficient of variation (CV)1.403825403
Kurtosis6.443053958
Mean72.24980496
Median Absolute Deviation (MAD)25.27256588
Skewness2.280482399
Sum35835.90326
Variance10287.25611
MonotonicityNot monotonic
2021-08-01T22:35:15.043113image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08
 
1.6%
5.8978294461
 
0.2%
2.1756434031
 
0.2%
26.176713141
 
0.2%
36.280635621
 
0.2%
271.1110081
 
0.2%
248.16336461
 
0.2%
91.178992191
 
0.2%
39.321054221
 
0.2%
85.766294621
 
0.2%
Other values (479)479
96.6%
ValueCountFrequency (%)
08
1.6%
0.00055813955711
 
0.2%
0.002356589211
 
0.2%
0.0030542635181
 
0.2%
0.0066821706011
 
0.2%
0.0096744185271
 
0.2%
0.011069767371
 
0.2%
0.012976744051
 
0.2%
0.017627906981
 
0.2%
0.020046511541
 
0.2%
ValueCountFrequency (%)
645.51575161
0.2%
615.00336381
0.2%
580.15551921
0.2%
491.21671341
0.2%
489.97463621
0.2%
459.34776781
0.2%
451.9545891
0.2%
418.60663541
0.2%
389.64088331
0.2%
373.5572711
0.2%

Maule
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct496
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.66277342
Minimum0
Maximum759.1777124
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-08-01T22:35:15.149721image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.7408652256
Q111.89736272
median45.64988768
Q3142.2307156
95-th percentile353.4197152
Maximum759.1777124
Range759.1777124
Interquartile range (IQR)130.3333528

Descriptive statistics

Standard deviation122.7667708
Coefficient of variation (CV)1.244306911
Kurtosis4.086940274
Mean98.66277342
Median Absolute Deviation (MAD)41.54554078
Skewness1.908906221
Sum48936.73562
Variance15071.68002
MonotonicityNot monotonic
2021-08-01T22:35:15.257446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.72466722161
 
0.2%
191.61204651
 
0.2%
57.517712121
 
0.2%
318.64134771
 
0.2%
385.24304551
 
0.2%
183.74356081
 
0.2%
34.775590721
 
0.2%
107.37418471
 
0.2%
2.6967054861
 
0.2%
291.85209641
 
0.2%
Other values (486)486
98.0%
ValueCountFrequency (%)
01
0.2%
0.002379367761
0.2%
0.0089683859991
0.2%
0.011921796921
0.2%
0.025457570661
0.2%
0.029692179521
0.2%
0.032163061551
0.2%
0.11556572381
0.2%
0.17856073061
0.2%
0.20048252911
0.2%
ValueCountFrequency (%)
759.17771241
0.2%
643.36747951
0.2%
577.71826121
0.2%
568.88982551
0.2%
545.90315321
0.2%
534.35638911
0.2%
532.56940111
0.2%
495.9735521
0.2%
482.19900921
0.2%
481.27215461
0.2%

Biobio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct496
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.1134058
Minimum0.00117021276
Maximum702.434707
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-08-01T22:35:15.371087image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.00117021276
5-th percentile5.195947475
Q129.35668384
median79.55855717
Q3184.7513066
95-th percentile403.1391807
Maximum702.434707
Range702.4335368
Interquartile range (IQR)155.3946228

Descriptive statistics

Standard deviation130.0560942
Coefficient of variation (CV)1.015163818
Kurtosis2.141751033
Mean128.1134058
Median Absolute Deviation (MAD)61.63801526
Skewness1.48873246
Sum63544.24928
Variance16914.58764
MonotonicityNot monotonic
2021-08-01T22:35:15.475844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.1729454821
 
0.2%
192.46552521
 
0.2%
62.112120981
 
0.2%
331.58385631
 
0.2%
461.29402241
 
0.2%
340.07574451
 
0.2%
65.433783271
 
0.2%
126.6055121
 
0.2%
0.5044614361
 
0.2%
266.83460781
 
0.2%
Other values (486)486
98.0%
ValueCountFrequency (%)
0.001170212761
0.2%
0.063710106131
0.2%
0.18313164971
0.2%
0.27109042551
0.2%
0.31540558521
0.2%
0.41501329921
0.2%
0.43657579641
0.2%
0.5044614361
0.2%
0.53049202411
0.2%
0.67627659481
0.2%
ValueCountFrequency (%)
702.4347071
0.2%
684.95850371
0.2%
617.3247141
0.2%
607.54048531
0.2%
563.84227461
0.2%
556.55946121
0.2%
526.33773251
0.2%
505.88192841
0.2%
489.73546491
0.2%
482.45396241
0.2%

La_Araucania
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct496
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.4864559
Minimum0.005331302395
Maximum661.2112029
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-08-01T22:35:15.581526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.005331302395
5-th percentile16.27306551
Q152.93034274
median110.5709901
Q3208.3162434
95-th percentile404.1772219
Maximum661.2112029
Range661.2058716
Interquartile range (IQR)155.3859006

Descriptive statistics

Standard deviation126.628503
Coefficient of variation (CV)0.835906433
Kurtosis1.248656826
Mean151.4864559
Median Absolute Deviation (MAD)72.76584167
Skewness1.236218224
Sum75137.28214
Variance16034.77778
MonotonicityNot monotonic
2021-08-01T22:35:15.682673image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.595833971
 
0.2%
191.18561321
 
0.2%
85.134082241
 
0.2%
379.06051021
 
0.2%
472.66121111
 
0.2%
346.0039911
 
0.2%
108.8578981
 
0.2%
166.67048741
 
0.2%
5.3332977871
 
0.2%
248.5932221
 
0.2%
Other values (486)486
98.0%
ValueCountFrequency (%)
0.0053313023951
0.2%
0.14697638991
0.2%
0.66154607841
0.2%
1.3732977931
0.2%
1.4816298561
0.2%
2.0811652691
0.2%
2.8347905521
0.2%
3.3727189651
0.2%
4.1143716631
0.2%
5.2949047971
0.2%
ValueCountFrequency (%)
661.21120291
0.2%
639.46125671
0.2%
580.08097541
0.2%
576.9744631
0.2%
564.76157661
0.2%
553.05334331
0.2%
538.36998491
0.2%
501.52643551
0.2%
486.31641291
0.2%
482.66698411
0.2%

Los_Rios
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct496
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.4517892
Minimum0.7481266175
Maximum717.7356727
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2021-08-01T22:35:15.783406image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.7481266175
5-th percentile37.33926037
Q189.80564282
median159.4679395
Q3274.5945898
95-th percentile475.61688
Maximum717.7356727
Range716.9875461
Interquartile range (IQR)184.7889469

Descriptive statistics

Standard deviation140.0736146
Coefficient of variation (CV)0.7022930963
Kurtosis0.6124866119
Mean199.4517892
Median Absolute Deviation (MAD)85.80759069
Skewness1.007050639
Sum98928.08742
Variance19620.6175
MonotonicityNot monotonic
2021-08-01T22:35:15.892115image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.268010381
 
0.2%
271.34444461
 
0.2%
122.71505181
 
0.2%
426.48886341
 
0.2%
549.41474171
 
0.2%
381.13210621
 
0.2%
134.40605951
 
0.2%
199.07527131
 
0.2%
24.393410831
 
0.2%
279.69640831
 
0.2%
Other values (486)486
98.0%
ValueCountFrequency (%)
0.74812661751
0.2%
1.4628165411
0.2%
1.6694573681
0.2%
4.6713565991
0.2%
6.8429844981
0.2%
14.011201541
0.2%
16.655490961
0.2%
17.266731291
0.2%
17.616395331
0.2%
20.32959951
0.2%
ValueCountFrequency (%)
717.73567271
0.2%
711.49502541
0.2%
669.55686031
0.2%
645.76359191
0.2%
642.54413481
0.2%
572.97276471
0.2%
569.96763531
0.2%
566.53816591
0.2%
551.56680851
0.2%
549.41474171
0.2%

Interactions

2021-08-01T22:35:08.280905image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:08.374666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:08.459440image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:08.546210image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:08.633973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:08.719743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:08.809535image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:08.898267image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:08.988026image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:09.065819image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:09.141714image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:09.217511image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:09.295303image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:09.383037image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:09.460829image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:09.537624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:09.614449image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:09.700189image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:09.778105image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:09.860884image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:09.944660image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:10.030431image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:10.115204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:10.201972image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:10.282756image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:10.377534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:10.460312image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:10.547049image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:10.635812image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:10.722612image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:10.813369image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:10.901134image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:10.993437image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:11.086723image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:11.168504image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:11.257298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:11.349161image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:11.441913image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:11.525689image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:11.613487image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:11.703245image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:11.791977image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:11.870766image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:11.957534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:12.042307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:12.126114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:12.210888image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:12.300654image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:12.385422image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:12.467183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:12.546003image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:12.632741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:12.718511image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:12.807285image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:12.891095image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:12.976831image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:13.064628image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:13.152362image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:13.235141image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:13.322938image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:13.407715image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:13.498437image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:13.583210image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-01T22:35:13.670975image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-08-01T22:35:15.986893image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-01T22:35:16.112539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-01T22:35:16.238235image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-01T22:35:16.361873image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-08-01T22:35:13.835554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-01T22:35:13.982609image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

dateCoquimboValparaisoMetropolitana_de_SantiagoLibertador_Gral__Bernardo_O_HigginsMauleBiobioLa_AraucaniaLos_Rios
01989-02-010.7190330.1170450.0282720.0000000.7246679.17294522.59583452.268010
11998-10-010.0652160.0000000.0114930.0000000.0089682.08967414.70208745.142041
22001-02-012.0300260.0479870.0026340.0000000.03216310.76835129.46505765.571098
32008-11-010.6227840.0000000.0158720.0000001.73416825.08355764.901645113.093488
42009-04-010.0080650.0000000.0093960.00000012.08071556.23342496.364235155.622235
52015-01-010.2393660.0256170.3654530.0000000.0119220.0011700.0053311.462817
62016-03-011.4103400.1737500.0568960.0000000.46460115.18888328.31485942.045866
72020-03-010.0034510.0006660.0000000.0000000.11556612.46269928.31093773.640866
81988-12-013.6053867.6864615.7642451.3533645.67035829.85512661.418941105.088928
92015-12-010.2727390.2830840.6887920.4103410.51331120.05459471.496481103.072700

Last rows

dateCoquimboValparaisoMetropolitana_de_SantiagoLibertador_Gral__Bernardo_O_HigginsMauleBiobioLa_AraucaniaLos_Rios
4862004-12-010.0505560.4385395.34699713.84141144.79874433.31447552.080335115.730698
4871998-04-0126.98434645.80003367.68956473.36316371.47552474.34590478.063648116.467493
4881991-10-012.24445817.26996836.33255052.35355080.46400282.24278677.135149109.656628
4892011-03-011.5530654.6842699.45006721.83240336.23003359.20089190.166794158.491137
4902018-03-010.0833331.5011533.83914417.90004741.16614087.381849144.213397250.525723
4911988-10-010.0000000.0198864.6217118.26099227.61698069.572460133.732635181.879767
4922019-08-010.0000000.4309904.5097489.90080649.285557108.404827155.448370233.621020
4932015-06-010.0000000.3257144.88870833.152636117.695499255.274275343.280205386.836253
4942016-09-010.0000000.0358601.0669803.09096118.73699745.64680274.499307119.114419
4951982-04-010.0000000.0196106.84860716.89175228.90493341.03529947.87076989.875426